Scene Background Blurring Including Range Measurement

ABSTRACT

Different distances of two or more objects in a scene being captured in a video conference are determined by determining a sharpest of two or more color channels and calculating distances based on the determining of the sharpest of the two or more color channels. At least one of the objects is identified as a foreground object or a background object, or one or more of each, based on the determining of the different distances. The technique involves blurring or otherwise rendering unclear at least one background object or one or more portions of the scene other than the at least one foreground object, or combinations thereof, also based on the determining of distances.

PRIORITY AND RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.12/883,192, filed Sep. 16, 2010; which claims priority to U.S.provisional patent application No. 61/361,868, filed Jul. 6, 2010. Thisapplication is one of a series of three contemporaneously-filedapplications, including those entitled SCENE BACKGROUND BLURRINGINCLUDING DETERMINING A DEPTH MAP (application Ser. No. 12/883,183),SCENE BACKGROUND BLURRING INCLUDING FACE MODELING (application Ser. No.12/883,191), AND SCENE BACKGROUND BLURRING INCLUDING RANGE MEASUREMENT(application Ser. No. 12/883,192).

BACKGROUND OF THE INVENTION

Video conference calls can be made using a wide variety of devices, suchas office video conferencing systems, personal computers, and telephonedevices including mobile telephones. Thus, video conferencing can beused at many different locations, including company offices, privateresidences, internet cafés and even on the street. The manypossibilities and varied locations for holding video conferences cancreate a problem since the video conference camera reveals the locationof the participant to all those watching or participating in the videoconference. For instance, if a video conference call is made from aparticipant's private place of residence, the participant's privacy maybe compromised since the participant's private environment and membersof his or her household may be exposed and photographed during the videoconference call. It is desired to be able to maintain the privacy andconfidentiality of other commercial issues that may inadvertentlyotherwise appear in the background of a video conference. It is desiredto have a technique that ensures that such items will not be revealed orshared during the video conference.

Range measurement is important in several applications, including axialchromatic aberration correction, surveillance means, and safety means.Active methods for calculating the distance between an object and ameasuring apparatus are usually based on the measurement of the timerequired for a reflected electro-magnetic or acoustic wave to reach andbe measured by measuring apparatus, e.g., sonar and radar. Activemethods of range measurement are detrimentally affected by physicalobjects present in the medium between the measuring apparatus and theobject. Current passive methods use an autofocus mechanism. However,determining the range typically involves varying the focal length bychanging lens position, which is not available in camera phones and manyother camera-enabled devices.

Digital cameras are usually equipped with iris modules designed tocontrol exposure, which are based on a detection result received fromthe sensor. Due to size and cost limitations, camera phones usually havefixed apertures and, hence, fixed F numbers. Existing mechanical irismodules are difficult to even incorporate in their simplest form intocamera phones due to increased price of optical module, increased formfactor since the iris module height is about 1 mm, greater mechanicalsensitivity, consumption of electrical power, and complex integration(yield).

Digital cameras are usually equipped with iris modules designed tocontrol exposure, which is based on a detection result received from asensor. Due to size and cost limitations, camera phones usually havefixed apertures and, hence, fixed F numbers. Mobile phone camerascommonly have apertures that provide F numbers in the range ofF/2.4-F/2.8. An advantage of the higher value, F/2.8, is mainly in itsimage resolution, but a drawback can be low performance under low lightconditions. The lower value, F/2.4, compromises depth of focus and imageresolution for a faster lens, i.e., better performance under low lightconditions. Alternatively, a ND filter may be used to control exposureinstead of changing F/#. Several high-end modules address theabove-mentioned problems using mechanically adjustable apertures.Incorporating iris modules into camera phones offers a variable F numberand achieves multiple advantages, including image quality improvementdue to reduced motion blur, improved SNR and improved resolution. Inaddition, incorporation of iris modules into camera phones can tend toimpart a digital still camera like “feel” due to the variable depth offield, i.e. Bokeh effect. Disadvantages of incorporating iris modulesinto camera phones include the increased price of the optical module,increased form factor due to the iris module height being about 1 mm,greater mechanical sensitivity, consumption of electrical power, andcomplex integration (yield). It is desired to have a digital iris thatenables the user to enjoy the advantages of the mechanical iris withoutits disadvantages and to experience the “feel” of a digital stillcamera.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

FIG. 1A illustrates a video conference display including a person's facein the foreground and clearly visible background items.

FIG. 1B illustrates a video conference display including a person'sface, neck and shoulders against a blurred background.

FIG. 2 is a flow chart of an exemplary method in accordance with certainembodiments.

FIG. 3 is a flow chart of an exemplary method in accordance with certainembodiments.

FIG. 4 illustrates a digital iris in accordance with certainembodiments.

FIG. 5 illustrates plots of calculated MTF curves per mm vs. defocusdistance for high F/# compared to low F/# for the same focal length.

FIG. 6 illustrates a digital iris in accordance with certainembodiments.

FIGS. 7A-7B illustrate plots of calculated MTF curves and through-focusMTF in accordance with certain embodiments.

FIGS. 8A-8B illustrate depth estimation using knowledge of longitudinalchromatic aberrations in accordance with certain embodiments.

FIG. 9 illustrates relative sharpness measurements during autofocusconvergence in accordance with certain embodiments.

FIG. 10 illustrates using depth map to control depth of focus inaccordance with certain embodiments.

FIG. 11 illustrates an extended depth of field using a digital iris inaccordance with certain embodiments.

FIG. 12 illustrates an auto focus mode using a digital iris inaccordance with certain embodiments.

FIG. 13 illustrates a Bokeh effect using a digital iris in accordancewith certain embodiments.

FIG. 14 illustrates a comparison of the three different modesillustrated in FIGS. 11-13.

FIG. 15 illustrates an extended depth of field using a digital iris innarrow aperture mode in accordance with certain embodiments.

FIG. 16 illustrates an auto-focus mode using a digital iris in wideaperture mode with focus at far in accordance with certain embodiments.

FIG. 17 illustrates a Bokeh effect using a digital iris in accordancewith certain embodiments.

FIG. 18 illustrates a comparison of the three different modesillustrated in FIGS. 15-17.

FIG. 19 illustrates extended depth of field using a digital iris innarrow aperture mode in accordance with certain embodiments.

FIG. 20 illustrates an auto-focus mode using a digital iris in wideaperture mode with focus at far in accordance with certain embodiments.

FIG. 21 illustrates a Bokeh effect using a digital iris in accordancewith certain embodiments.

FIG. 22 illustrates a comparison of the three different modesillustrated in FIGS. 19-21.

DETAILED DESCRIPTIONS OF THE EMBODIMENTS

A method is provided to display a participant during a video conferenceagainst a blurred or otherwise unclear background. The method accordingto certain embodiments involves determining different distances of twoor more objects in a scene being captured in video, including performingan auto-focus sweep of the scene. A depth map of the scene is generatedbased on the auto-focus sweep. At least one of the objects is identifiedas a foreground object or a background object, or one or more of each,based on the determining of the different distances. The method furtherinvolves blurring or otherwise rendering unclear at least one backgroundobject and/or one or more portions of the scene other than the at leastone foreground object, also based on the determining of distances.

A further method is provided, e.g., as illustrated in the flowchart ofFIG. 2, to display a participant during a video conference against ablurred or otherwise unclear background. The method includes determiningdifferent distances of two or more objects in a scene being captured invideo, including determining a sharpest of two or more color channelsand calculating distances based on the determining of the sharpest ofthe two or more color channels. At least one of the objects isidentified as a foreground object or a background object, or one or moreof each are identified, based on the determining of the differentdistances. The method further includes blurring or otherwise renderingunclear at least one background object or one or more portions of thescene other than the at least one foreground object, or combinationsthereof, also based on the determining of distances.

A face may be detected within the scene and designating as a foregroundobject. An audio or visual parameter of the face, or both, may beenhanced, such as, e.g., loudness, audio tone, or sound balance of wordsbeing spoken by a person associated with the face, or enhancingluminance, color, contrast, or size or location within the scene of theface, or combinations thereof. The method may include recognizing andidentifying the face as that of a specific person, and the face may betagged with a stored identifier. A nearest object may be designated as aforeground object. One or more objects may be designated as backgroundthat are at a different distance than a foreground object. A nearestobject or a detected face, or both, may be designated as the foregroundobject. The determining of the different distances may involve use of afixed focus lens. A portion of the scene other than a foreground objectmay include a detected and recognized face or other object, and themethod may also include determining that the recognized face or otherobject is private (and, e.g., made subject to being blurred or otherwiserendered unclear). The distances may include a distance between a videocamera component and at least one of the two or more objects in thescene. One or more distances may be determined based on applying a facemodel to a detected face within the scene. The determining of thesharpest of two or more color channel may involve calculating thefollowing:

$\begin{matrix}{{sharpest} = \{ {{j\; 1\frac{\sigma_{j}}{{AV}_{i}}} = {\max \{ {\frac{\sigma_{r}}{{AV}_{r}};\frac{\sigma_{s}}{{AV}_{s}};\frac{\sigma_{b}}{{AV}_{b}}} \}}} \}} & ( 3 \end{matrix}$

where AVi comprise averages of pixels for the three color channels {j|r,g, b}, and may further involve calculating one or both of the following:

$\begin{matrix}{{\sigma_{i} = \sqrt{\frac{1}{N}\text{?}{\sum( {i - {AV}_{i}} )^{2}}}}{{{where}\mspace{14mu} i} \in \{ {R,G,B} \}}{Or}} & ( 1  \\{{{\sigma_{i} \cong {\frac{1}{N}\text{?}{\sum{{i - {AV}_{i}}}}}}{{{where}\mspace{14mu} i} \in \{ {R,G,B} \}}{\text{?}\text{indicates text missing or illegible when filed}}}\mspace{284mu}} & ( 2 \end{matrix}$

Another method is provided, e.g., as illustrated in the flowchart ofFIG. 3, to display a participant during a video conference against ablurred or otherwise unclear background. A face is detected within adigitally-acquired image. A face model is applied to the face. Adistance of the face from a video camera component is determined basedon the applying of the face model. At least one portion of the sceneother than the face is identified as including a background object thatis a different distance from the video camera component than the face.The background object is blurring or otherwise rendered unclear.

An audio or visual parameter of the face, or both, may be enhanced, suchas, e.g., loudness, audio tone, or sound balance of words being spokenby a person associated with the face, or enhancing luminance, color,contrast, or size or location within the scene of the face, orcombinations thereof. The method may include recognizing and identifyingthe face as that of a specific person, and the face may be tagged with astored identifier.

The method may further include increasing a size of the face orcentering the face, or both. Any one or more of brightness, luminancecontrast, color or color balance of the face may be enhanced. Thedetermining of the distance of the face from the video camera componentmay include determining one or more distances and/or other geometriccharacteristics of detected face features. The determining of thedistance of the face from the video camera component may involvedetermining a sharpest of two or more color channels and calculating thedistance based on the determining of the sharpest of the two or morecolor channels. The determining of the different distances may involveuse of a fixed focus lens.

The determining of the sharpest of two or more color channel may involvecalculating the following:

$\begin{matrix}{{sharpest} = \{ {{j\; 1\frac{\sigma_{j}}{{AV}_{i}}} = {\max \{ {\frac{\sigma_{r}}{{AV}_{r}};\frac{\sigma_{s}}{{AV}_{s}};\frac{\sigma_{b}}{{AV}_{b}}} \}}} \}} & ( 3 \end{matrix}$

where AVi comprise averages of pixels for the three color channels {j|r,g, b}, and may further involve calculating one or both of the following:

$\begin{matrix}{{\sigma_{i} = \sqrt{\frac{1}{N}\text{?}{\sum( {i - {AV}_{i}} )^{2}}}}{{{where}\mspace{14mu} i} \in \{ {R,G,B} \}}{Or}} & ( 1  \\{{{\sigma_{i} \cong {\frac{1}{N}\text{?}{\sum{{i - {AV}_{i}}}}}}{{{where}\mspace{14mu} i} \in \{ {R,G,B} \}}{\text{?}\text{indicates text missing or illegible when filed}}}\mspace{284mu}} & ( 2 \end{matrix}$

One or more computer-readable storage media having code embedded thereinfor programming a processor to perform any of the methods describedherein.

A video conferencing apparatus is also provided, including a videocamera including a lens, and an image sensor, a microphone, a display, aprocessor, one or more networking connectors, and a memory having codeembedded therein for programming a processor to perform any of themethods described herein.

Scene Background Blurring

A method is provided that enables video conference participants to beseen in focus while the rest of the scene around them is blurred. Thus,participants can maintain their privacy and confidentiality of othercommercial issues they do not wish to reveal or share. The method mayinclude face identification of the participant and an estimation of thedistance between the participant and the lens, or alternatively, theidentification of those objects that are at a distance from theparticipant.

The method advantageously permits the maintenance of privacy ofparticipants in video conferences, safeguards confidential information,and enables such calls to be made from any location without divulgingthe exact nature of the location from which the call is being made.Another advantage is the ability to use an existing face identificationsoftware package.

Embodiments are described that solve the above-mentioned problems ofmaintaining privacy in video conferencing, namely scene backgroundblurring or SBB. Scene background blurring is based on the real-timeestimation of the distance between objects in the scene. Specifically,the method may involve estimating the distance between the camera lensand the location of the person participating in the video conferencecall. Using image processing and the knowledge of this distance, it ispossible to blur all other details that are located at a greater (and/orlesser) distance from the lens (see FIGS. 1A-1B). In order to estimatethe distance between the video conference participant and the cameralens, face identification software may be used to identify theparticipant's location and then to estimate the participant's distancefrom the lens. Alternatively, the system can determine which of theobjects are farther away from the lens than the participant. Thus, it ispossible to selectively blur the information that is farther away(and/or closer) than the participant. The distance from the lens to theparticipant or the relative distance between the objects and theparticipant can be determined using various optical properties asdescribed hereinbelow. For example, a method that uses the relationbetween the focal length and the dispersion of the lens material, i.e.,the variation of the refractive index, n, with the wavelength of light,may be used. The different position of the focal plane for differentcolors enables a determination of the distance of an object from thelens. It is also possible to utilize the eyes distance which is known tobe 6-7 cm, or another geometric face feature or human profile feature,for estimating the relative distance of the participant. Other opticalproperties can also be used to determine which objects are farther awaythan the person identified. This can be achieved as part of an opticalsystem that includes both image processing and the SBB or as part of asoftware system that can be implemented flexibly in cameras used forvideo conferencing.

Sharp, selective imaging of the participant or any other element of theimage may be provided in a video conference, while the more distantenvironment may be blurred (and/or closer objects like desk items andthe like). The method may involve face identification of the participantand an estimation of the distance between the participant and the cameralens, or alternatively, identification of objects that are at adifferent distance from the participant.

Range Measurement Applied on a Bayer Image Pattern

The dependence of focal length on the dispersion of the lens material ofa camera is used in certain embodiments. This dependence has to do withthe variation of the refractive index n with wavelengths of light. Thevariation of the focal length for different colors provides a sharpchannel (one of the R, G or B channels), while the rest of the channelsare blurry. This enables at least a rough determination of the distanceof an object from the camera lens.

Unlike active methods of range measurement, passive methods are lessaffected by physical objects (such as window panes or trees) that may bepresent in the medium between the measuring apparatus and the object.Moreover, passive methods tend to be more accurate. It is alsoadvantageous for a method that it is to be part of an ISP chain to workdirectly on a Bayer Image pattern, because there is significantly moreflexibility in the placement of the block within the ISP chain.Moreover, ranges can be roughly determined with a fixed focus lens. Apassive method for range measurement in accordance with certainembodiments uses dispersion means, i.e., involves finding a sharpestchannel between the R, G, and B color channels.

Embodiments are described herein of passive range measurement techniquesthat operate on a Bayer pattern, thus combining both advantages. In oneexample, a 9×9 Bayer window may be used, and three colors (R, G, and B)may be used, although different windows and different combinations oftwo or more colors may be used. In one embodiment, an expansion to fourcolors (R, Gr, Gb, B) may be involved, whereby Gr are the green pixelsin a red line and Gb are the green pixels in a blue line.

Three averages may be calculated for the red, green, and blue pixelsrespectively (AVr, AVg, AVg). A measure of the amount of information maybe calculated. Such a measure may be obtained, for instance, withoutloss of generality, by calculating the standard deviation or the averageabsolute deviation of each color (see Equations 1 and 2 below). Then, asharpness measure may be derived, e.g., defined by σj/AVj and thesharpest color is chosen (see Equation 3 below). For far objects, thevast majority of results from Step 3 are ‘j=R’ while for close objects,the vast majority of results are ‘j=B’. If most of the results are‘j=G’, the object is located at mid-range.

The range measurement can be refined even further since the transitionfrom close to mid-range and then to far-range may be gradual. Thereforeit is expected that in regions that are between close- and mid-range, amixture of j=B and j=G will be obtained, while in regions betweenmid-range and far-range, a mixture of j=B and j=G will predominate. Itis therefore possible to apply statistics, (the probability that acertain color channel will be the sharpest within a certain region), inorder to more accurately determine the distance between an object andthe lens.

The following equations may be used in a passive method for rangemeasurement applied directly on a BAYER image pattern. The threeaverages of the red green and blue pixels may be respectively referredto as (AVr, AVg, AVb).

The measure for the amount of information may be given, without loss ofgenerality, by the following examples:

$\begin{matrix}{{\sigma_{i} = \sqrt{\frac{1}{N}\text{?}{\sum( {i - {AV}_{i}} )^{2}}}}{{{where}\mspace{14mu} i} \in \{ {R,G,B} \}}{Or}} & ( 1  \\{{{\sigma_{i} \cong {\frac{1}{N}\text{?}{\sum{{i - {AV}_{i}}}}}}{{{where}\mspace{14mu} i} \in \{ {R,G,B} \}}{\text{?}\text{indicates text missing or illegible when filed}}}\mspace{284mu}} & ( 2 \end{matrix}$

The sharpest channel may be provided by:

$\begin{matrix}{{sharpest} = \{ {{j\; 1\frac{\sigma_{j}}{{AV}_{i}}} = {\max \{ {\frac{\sigma_{r}}{{AV}_{r}};\frac{\sigma_{s}}{{AV}_{s}};\frac{\sigma_{b}}{{AV}_{b}}} \}}} \}} & ( 3 \end{matrix}$

Digital IRIS

A digital iris system in accordance with certain embodiments can achievethe effect of variable F/#. In addition, the system takes advantage oflow F/# in low-light captures, creating effects such as the Bokeh effect(which generally is not achieved with a typical mechanical camera phoneiris of F/2.4-4.8). This system enables users to enhance theirexperience by controlling depth of field. Additional advantages of thesystem include lower cost, lower module height, lower complexity, andgreater robustness.

The digital iris enables the user to enjoy, on a device that does notinclude a mechanical iris, the advantages of a device that includes amechanical iris without its disadvantages, and to experience the “feel”of a digital still camera. Those advantages include better performancein low-light environments, elimination of motion blur, and improvedsignal-to-noise ratio (SNR). Additional advantages of the system includelower cost, lower module height, lower complexity, and greaterrobustness.

A digital iris is provided in accordance with certain embodiments thatacts with respect to a subject image, and performs advantageous digitalexposure of one or more desired portions of the subject to bephotographed. Advantages include better performance in low-lightenvironments, elimination of motion blur, and improved SNR. Under goodlight conditions, a large depth of field is obtained, which can becontrolled by the user. Users' experiences can be enhanced by the Bokeheffect, whereby the background of a photo is out of focus, while a blureffect has a unique aesthetic quality.

Two distinct possibilities for lens design are related to their F/#values, which are closely connected to the exposure value. The F numberis defined as the focal length divided by the effective aperturediameter (f_eff/D). Each f-stop (exposure value) halves the lightintensity relative to the previous stop. For the case of Low-F/# lenses(wide aperture), advantages include short exposure time, less motionblur, high resolution at focus, reduced depth of field—Bokeh effect, andimproved low-light performance (less noise for the same exposure time).In certain embodiments, disadvantages such as tighter manufacturingtolerances, flare due to manufacturing errors, and diminished depth offield (with the lack of AF technology) are reduced or eliminated. Forthe case of high-F/# lenses (narrow aperture), advantages include largedepth of field, improved low-frequency behavior (contrast), reducedflare, finer saturated edges, and relaxed manufacturing tolerances. Incertain embodiments, disadvantages such as long exposure time, motionblur, and low-light noise performance are reduced or eliminated.

A digital iris in accordance with certain embodiments is illustrated atFIG. 4, which shows a calculated through-focus MTF at a spatialfrequency of 180 cycles per mm versus defocus distance (in units ofmillimeters) for high F/# compared to low F/# both for the same focallength. In FIG. 4, the arrows superimposed on the through-focus MTFcorrespond to a delimit range of defocus distance over which the MTF isgreater than 0.15. The defocus distances include the depth of field overwhich the range of defocus distances provide a contrast that issufficient for resolving the image. FIG. 4 exhibits an enhanced depth offocus for the case of the higher F/#. Our results show that the DOFdepends linearly on the F/#. FIG. 5 shows plots of calculated MTF curvesfor the imaging lens design for object distance of about 1 m atdifferent light wavelengths. The obtained resolution limit is found tobe inversely proportional to the F/# of the lens. The lower F/# lensesreach higher spatial resolution, but field dependency is larger (mainlythe tangential components).

As an example, a digital iris may be addressed for an F number of F/2.4in certain embodiments. The lens may be designed with a wide aperturelens, i.e. low F number of F/2.4, where the reduced DOF (see FIG. 4) isextended to F/4.8 using a technique such as that described at US patentapplication serial number PCT/US08/12670, filed Nov. 7, 2008 based onU.S. provisional Ser. No. 61/002,262, filed Nov. 7, 2007, entitled“CUSTOMIZED DEPTH OF FIELD OPTICAL SYSTEM”, which are assigned to thesame assignee and are hereby incorporated by reference. FIG. 6illustrates a simplified block diagram of a digital iris architecture inaccordance with this exemplary embodiment. As illustrated at FIG. 6, thedigital iris comprises in this example three independent components: alens 2 with low F/# and extended depth of field (EDoF) standardmechanical AF engine 4, and Image processing algorithm block 6 withpre-processing 8, depth estimation 10 that generates a depth map of theimage. Also, a focus engine 12 may be used to support the EDoF and alsoto provide a digital aesthetic blur function, and post-processing 14.

FIG. 7A-7B illustrates plots of calculated MTF curves and thethrough-focus MTFs in accordance with certain embodiments. FIGS. 7A-7Bshow that for F/2.4 the calculated through-focus MTF at a spatialfrequency of 180 cycles per mm the MTF is extended compared to thatshown in FIG. 4. The corresponding delimit range of defocus distanceover which the MTF is greater than 0.15, becomes in accordance withcertain embodiments almost equal to that of F/4.8 lens as is illustratedat FIG. 4. The calculated MTF curves keep the value that enables highspatial resolution as illustrated at FIG. 5 for the lens with F/2.4.Estimation depth is useful for the digital iris application. Under goodlight conditions, a large depth of field may be obtained, which can becontrolled by the user. For each pixel used for the depth map, it can bedetermined where it is in focus and what the distance of it is from afinally selected focus plan.

This distance will determine how much blur to introduce to this pixelwhen the digital post processing is applied. In the present embodimentof the digital iris, the focus plane may be determined by auto focus(AF) engine 4 (see FIG. 6), according to user preference.

One example approach that may be used for generating the depth mapinclude using the focal length dependence on the dispersion of the lensmaterial i.e. the variation of the refractive index, n, with thewavelength of light. The different position of the focal plan fordifferent colors enables a determination of a range of an object fromthe lens, see FIGS. 8A and 8B. This technique is passive and operates onthe BAYER pattern, i.e., the three colors (R, G and B). Another exampleapproach uses relative sharpness measurements during auto-focus (AF)convergence (with subsampled images), and is illustrated at FIG. 9.Statistics are collected at 22 in this embodiment at multiple locationsover the field of view (FOV). Best-focus positions for each region ofinterest (ROI) are determined at 24. A depth map is generated at 26.Sharpness statistics may be collected on a preview stream during an AFconvergence process at various image locations. A best focus positionmay be estimated for each location by comparing sharpness at differentfocus positions. After AF converges, the depth map may be calculatedfrom the focus position measurements.

FIG. 10 shows as an example a depth map 26 that is used to control depthof focus (DOF) via focus engine 28 in accordance with an embodiment ofthe digital iris system. The depth map information may be input to thefocus engine 28 along with user-selected F/#. This focus engine may beused to enhance sharpness or artistically blur the image to achieve adesired effect. Wide-DOF (large F/#) may be generated by applying astandard focus algorithm. Narrow-DOF (small F/#) may be generated bysomewhat blurring objects outside the focus plane. Bokeh effect isachieved 30 by applying large blur.

FIGS. 11-22 now illustrate multiple examples of images taken with threemodules. FIGS. 11-13 present images obtained with three differentmodules, whereas FIG. 14 presents a comparison between them whichdisplays the advantage of the digital iris with EDoF. FIGS. 15-17present a comparison between narrow apertures, a wide aperture and theBokeh effect. FIG. 18 presents a comparison between them which displaysthe advantage of the digital iris with EDoF. FIGS. 18-21 present anothercomparison to emphasize the advantage of the digital iris. FIG. 22presents a comparison between them which displays the advantage of thedigital iris with EDoF.

The digital iris may be based on a low F/# lens design with extendeddepth of field. Digital processing modes of low F/# mode to reduce thedepth of field of the lens and large F/# mode to keep the extended depthof field, as well as Bokeh mode are all advantageous. An estimationdepth map may be generated by relative sharpness measurements during AFconvergence and/or based on the focal length dependence on thedispersion of the lens material.

In certain embodiments, a method of displaying a participant during avideo conference against a blurred or otherwise unclear background isprovided. Distances are determined of two or more objects in a scenebeing captured in video. The method may include identifying at least oneof the objects as a foreground object based on the determining ofdistances, and/or blurring or otherwise rendering unclear one or moreportions of the scene other than the at least one foreground object alsobased on the determining of distances.

In certain embodiments, a method of displaying a participant during avideo conference against a blurred or otherwise unclear background isfurther provided. Distances are determined of two or more objects in ascene being captured in video. The method may further includeidentifying at least one of the objects as a background object based onthe determining of distances, and/or blurring or otherwise renderingunclear the at least one background object based on the determining ofdistances.

A face may be detected within the scene and designated as a foregroundobject. A nearest object may be designated as a foreground object. Oneor more objects may be designated as background that are at a differentdistance than a foreground object. A nearest object or a detected face,or both, may be designated as foreground objects. The determiningdistances may involve determining a sharpest of two or more colorchannels and calculating distances based on the determining of thesharpest of the two or more color channels.

While exemplary drawings and specific embodiments of the presentinvention have been described and illustrated, it is to be understoodthat that the scope of the present invention is not to be limited to theparticular embodiments discussed. Thus, the embodiments shall beregarded as illustrative rather than restrictive, and it should beunderstood that variations may be made in those embodiments by workersskilled in the arts without departing from the scope of the presentinvention.

In addition, in methods that may be performed according to preferredembodiments herein and that may have been described above, theoperations have been described in selected typographical sequences.However, the sequences have been selected and so ordered fortypographical convenience and are not intended to imply any particularorder for performing the operations, except for those where a particularorder may be expressly set forth or where those of ordinary skill in theart may deem a particular order to be necessary.

In addition, all references cited above and below herein, as well as thebackground, invention summary, abstract and brief description of thedrawings, are all incorporated by reference into the detaileddescription of the preferred embodiments as disclosing alternativeembodiments.

The following are incorporated by reference: U.S. Pat. Nos. 7,715,597,7,702,136, 7,692,696, 7,684,630, 7,680,342, 7,676,108, 7,634,109,7,630,527, 7,620,218, 7.606,417, 7,587,068, 7,403,643, 7,352,394,6,407,777, 7,269,292, 7,308,156, 7,315,631, 7,336,821, 7,295,233,6,571,003, 7,212,657, 7,039,222, 7,082,211, 7,184,578, 7,187,788,6,639,685, 6,628,842, 6,256,058, 5,579,063, 6,480,300, 5,781,650,7,362,368, 7,551,755, 7,515,740, 7,469,071 and 5,978,519; and

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U.S. patent applications Nos. 60/829,127, 60/914,962, 61/019,370,61/023,855, 61/221,467, 61/221,425, 61/221,417, 61/182,625, 61/221,455,61/091,700, 61/120,289, and Ser. No. 12/479,658.

1. (canceled)
 2. A method of displaying a participant during a videoconference against a blurred or otherwise unclear background,comprising: using an imaging device including an optic, an image sensorand a processor; determining different distances of two or more objectsin a scene being captured in video, including: performing an auto-focussweep of the scene; increasing a depth of field (DOF) includingextending a delimit range of defocus distance over which a mean transferfunction (MTF) is greater than 0.15 without decreasing aperture whilemaintaining a focus on said at least one foreground object atapproximately said determined distance; and generating a depth map ofthe scene based on the auto-focus sweep; and identifying at least one ofthe objects as a foreground object or a background object, or one ormore of each, based on the determining of the different distances; andblurring or otherwise rendering unclear the background object.
 3. Themethod of claim 2, further comprising detecting a face within the sceneand designating the face as a foreground object.
 4. The method of claim3, further comprising enhancing an audio or visual parameter of theface, or both.
 5. The method of claim 4, further comprising enhancingloudness, audio tone, or sound balance of words being spoken by a personassociated with the face, or enhancing luminance, color, contrast, orsize or location within the scene of the face, or combinations thereof.6. The method of claim 3, further comprising recognizing and identifyingthe face as that of a specific person.
 7. The method of claim 6, furthercomprising tagging the face with a stored identifier.
 8. The method ofclaim 2, further comprising designating a nearest object as a foregroundobject.
 9. The method of claim 2, further comprising designating one ormore objects as background that are at a different distance than aforeground object.
 10. The method of claim 9, further comprisingblurring or otherwise rendering unclear at least one background objector one or more portions of the scene other than the at least oneforeground object, or combinations thereof, also based on thedetermining of distances.
 11. The method of claim 2, wherein thedetermining of the different distances comprises using a fixed focuslens.
 12. The method of claim 2, wherein a portion of the scene otherthan a foreground object comprises a detected and recognized face orother object, and the method further comprises determining that therecognized face or other object is private.
 13. The method of claim 2,wherein said distances comprises a distance between a video cameracomponent and at least one of the two or more objects in the scene. 14.The method of claim 2, further comprising determining at least onedistance based on applying a face model to a detected face within thescene.
 15. The method of claim 2, wherein the determining distancescomprises calculating distances based on the determining of the sharpestof the two or more color channels.
 16. The method of claim 15, whereinthe determining of the sharpest of two or more color channel comprisescalculating the following for three color channels: $\begin{matrix}{{sharpest} = \{ {{j\; 1\frac{\sigma_{j}}{{AV}_{i}}} = {\max \{ {\frac{\sigma_{r}}{{AV}_{r}};\frac{\sigma_{s}}{{AV}_{s}};\frac{\sigma_{b}}{{AV}_{b}}} \}}} \}} & ( 3 \end{matrix}$ where AVi comprise averages of pixels for the three colorchannels {j|r, g, b}.
 17. The method of claim 16, wherein thedetermining of the sharpest of two or more color channel furthercomprises calculating the following: $\begin{matrix}{{\sigma_{i} = \sqrt{\frac{1}{N}\text{?}{\sum( {i - {AV}_{i}} )^{2}}}}{{{where}\mspace{14mu} i} \in \{ {R,G,B} \}}{Or}} & ( 1  \\{{{\sigma_{i} \cong {\frac{1}{N}\text{?}{\sum{{i - {AV}_{i}}}}}}{{{where}\mspace{14mu} i} \in \{ {R,G,B} \}}{\text{?}\text{indicates text missing or illegible when filed}}}\mspace{284mu}} & ( 2 \end{matrix}$
 18. One or more non-transitory computer-readable storagemedia having code embedded therein for programming a processor toperform a method of displaying a participant during a video conferenceagainst a blurred or otherwise unclear background, wherein the methodcomprises: determining different distances of two or more objects in ascene being captured in video, including: performing an auto-focus sweepof the scene; increasing a depth of field (DOF) including extending adelimit range of defocus distance over which a mean transfer function(MTF) is greater than 0.15 without decreasing aperture while maintaininga focus on said at least one foreground object at approximately saiddetermined distance; generating a depth map of the scene based on theauto-focus sweep; identifying at least one of the objects as aforeground object or a background object, or one or more of each, basedon the determining of the different distances; and blurring or otherwiserendering unclear the background object.
 19. The one or morecomputer-readable storage media of claim 18, wherein the method furthercomprises detecting a face within the scene and designating the face asa foreground object.
 20. The one or more computer-readable storage mediaof claim 19, wherein the method further comprises enhancing an audio orvisual parameter of the face, or both.
 21. The one or morecomputer-readable storage media of claim 20, wherein the method furthercomprises enhancing loudness, audio tone, or sound balance of wordsbeing spoken by a person associated with the face, or enhancingluminance, color, contrast, or size or location within the scene of theface, or combinations thereof.
 22. The one or more computer-readablestorage media of claim 19, wherein the method further comprisesrecognizing and identifying the face as that of a specific person. 23.The one or more computer-readable storage media of claim 22, wherein themethod further comprises tagging the face with a stored identifier. 24.The one or more computer-readable storage media of claim 18, wherein themethod further comprises designating a nearest object as a foregroundobject.
 25. The one or more computer-readable storage media of claim 18,wherein the method further comprises designating one or more objects asbackground that are at a different distance than a foreground object.26. The one or more computer-readable storage media of claim 25, whereinthe method further comprises blurring or otherwise rendering unclear atleast one background object or one or more portions of the scene otherthan the at least one foreground object, or combinations thereof, alsobased on the determining of distances.
 27. The one or morecomputer-readable storage media of claim 18, wherein the determining ofthe different distances comprises using a fixed focus lens.
 28. The oneor more computer-readable storage media of claim 18, wherein a portionof the scene other than a foreground object comprises a detected andrecognized face or other object, and the method further comprisesdetermining that the recognized face or other object is private.
 29. Theone or more computer-readable storage media of claim 18, wherein saiddistances comprises a distance between a video camera component and atleast one of the two or more objects in the scene.
 30. The one or morecomputer-readable storage media of claim 18, wherein the method furthercomprises determining at least one distance based on applying a facemodel to a detected face within the scene.
 31. The one or morecomputer-readable storage media of claim 18, wherein the determiningdistances comprises calculating distances based on the determining ofthe sharpest of the two or more color channels.
 32. The one or morecomputer-readable storage media of claim 31, wherein the determining ofthe sharpest of two or more color channel comprises calculating thefollowing: $\begin{matrix}{{sharpest} = \{ {{j\; 1\frac{\sigma_{j}}{{AV}_{i}}} = {\max \{ {\frac{\sigma_{r}}{{AV}_{r}};\frac{\sigma_{s}}{{AV}_{s}};\frac{\sigma_{b}}{{AV}_{b}}} \}}} \}} & ( 3 \end{matrix}$ Where AVi comprise averages of pixels for the three colorchannels {j|r, g, b}
 33. The one or more computer-readable storage mediaof claim 32, wherein the determining of the sharpest of two or morecolor channel further comprises calculating the following:$\begin{matrix}{{\sigma_{i} = \sqrt{\frac{1}{N}\text{?}{\sum( {i - {AV}_{i}} )^{2}}}}{{{where}\mspace{14mu} i} \in \{ {R,G,B} \}}{Or}} & ( 1  \\{{{\sigma_{i} \cong {\frac{1}{N}\text{?}{\sum{{i - {AV}_{i}}}}}}{{{where}\mspace{14mu} i} \in \{ {R,G,B} \}}{\text{?}\text{indicates text missing or illegible when filed}}}\mspace{284mu}} & ( 2 \end{matrix}$
 34. A video conferencing apparatus, comprising: a videocamera including a lens, and an image sensor; a microphone; a display; aprocessor; one or more networking connectors; and one or morecomputer-readable storage media having code embedded therein forprogramming a processor to perform a method of displaying a participantduring a video conference against a blurred or otherwise unclearbackground, wherein the method comprises: determining differentdistances of two or more objects in a scene being captured in video,including: performing an auto-focus sweep of the scene; increasing adepth of field (DOF) including extending a delimit range of defocusdistance over which a mean transfer function (MTF) is greater than 0.15without decreasing aperture while maintaining a focus on said at leastone foreground object at approximately said determined distance;generating a depth map of the scene based on the auto-focus sweep;identifying at least one of the objects as a foreground object or abackground object, or one or more of each, based on the determining ofthe different distances; and blurring or otherwise rendering unclear thebackground object.
 35. The apparatus of claim 34, wherein the methodfurther comprises detecting a face within the scene and designating theface as a foreground object.
 36. The apparatus of claim 35, wherein themethod further comprises enhancing an audio or visual parameter of theface, or both.
 37. The apparatus of claim 36, wherein the method furthercomprises enhancing loudness, audio tone, or sound balance of wordsbeing spoken by a person associated with the face, or enhancingluminance, color, contrast, or size or location within the scene of theface, or combinations thereof.
 38. The apparatus of claim 33, whereinthe method further comprises recognizing and identifying the face asthat of a specific person.
 39. The apparatus of claim 38, wherein themethod further comprises tagging the face with a stored identifier. 40.The apparatus of claim 34, wherein the method further comprisesdesignating a nearest object as a foreground object.
 41. The apparatusof claim 34, wherein the method further comprises designating one ormore objects as background that are at a different distance than aforeground object.
 42. The apparatus of claim 41, wherein the methodfurther comprises blurring or otherwise rendering unclear at least onebackground object or one or more portions of the scene other than the atleast one foreground object, or combinations thereof, also based on thedetermining of distances.
 43. The apparatus of claim 34, wherein thedetermining of the different distances comprises using a fixed focuslens.
 44. The apparatus of claim 34, wherein a portion of the sceneother than a foreground object comprises a detected and recognized faceor other object, and the method further comprises determining that therecognized face or other object is private.
 45. The apparatus of claim34, wherein said distances comprises a distance between a video cameracomponent and at least one of the two or more objects in the scene. 46.The apparatus of claim 34, wherein the method further comprisesdetermining at least one distance based on applying a face model to adetected face within the scene.
 47. The apparatus of claim 34, whereinthe determining distances comprises calculating distances based on thedetermining of the sharpest of the two or more color channels.
 48. Theapparatus of claim 47, wherein the determining of the sharpest of two ormore color channel comprises calculating the following: $\begin{matrix}{{sharpest} = \{ {{j\; 1\frac{\sigma_{j}}{{AV}_{i}}} = {\max \{ {\frac{\sigma_{r}}{{AV}_{r}};\frac{\sigma_{s}}{{AV}_{s}};\frac{\sigma_{b}}{{AV}_{b}}} \}}} \}} & ( 3 \end{matrix}$ Where AVi comprise averages of pixels for the three colorchannels {j|r, g, b}
 49. The apparatus of claim 48, wherein thedetermining of the sharpest of two or more color channel furthercomprises calculating the following: $\begin{matrix}{{\sigma_{i} = \sqrt{\frac{1}{N}\text{?}{\sum( {i - {AV}_{i}} )^{2}}}}{{{where}\mspace{14mu} i} \in \{ {R,G,B} \}}{Or}} & ( 1  \\{{{\sigma_{i} \cong {\frac{1}{N}\text{?}{\sum{{i - {AV}_{i}}}}}}{{{where}\mspace{14mu} i} \in \{ {R,G,B} \}}{\text{?}\text{indicates text missing or illegible when filed}}}\mspace{284mu}} & ( 2 \end{matrix}$